Characterization of an aspartate aminotransferase encoded by YPO0623 with frequent nonsense mutations in Yersinia pestis

Yersinia pestis, the causative agent of plague, is a genetically monomorphic bacterial pathogen that evolved from Yersinia pseudotuberculosis approximately 7,400 years ago. We observed unusually frequent mutations in Y. pestis YPO0623, mostly resulting in protein translation termination, which implies a strong natural selection. These mutations were found in all phylogenetic lineages of Y. pestis, and there was no apparent pattern in the spatial distribution of the mutant strains. Based on these findings, we aimed to investigate the biological function of YPO0623 and the reasons for its frequent mutation in Y. pestis. Our in vitro and in vivo assays revealed that the deletion of YPO0623 enhanced the growth of Y. pestis in nutrient-rich environments and led to increased tolerance to heat and cold shocks. With RNA-seq analysis, we also discovered that the deletion of YPO0623 resulted in the upregulation of genes associated with the type VI secretion system (T6SS) at 26°C, which probably plays a crucial role in the response of Y. pestis to environment fluctuations. Furthermore, bioinformatic analysis showed that YPO0623 has high homology with a PLP-dependent aspartate aminotransferase in Salmonella enterica, and the enzyme activity assays confirmed its aspartate aminotransferase activity. However, the enzyme activity of YPO0623 was significantly lower than that in other bacteria. These observations provide some insights into the underlying reasons for the high-frequency nonsense mutations in YPO0623, and further investigations are needed to determine the exact mechanism

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

RETRACTED ARTICLE: Financing mode in China's transition period

Financing is an effective way to satisfy different needs of both capital supply and demand. Different financing modes determine the financing efficiency directly. In various countries around the world, their distinctive financing modes depend on their market economy system and financing system. For China, a country during economic transition period, it is of great significance to alter the existing inefficient financing mode and enhance the financing efficiency. Based on the comparative analysis of the two kinds of prevailing overseas financing modes, this paper discusses the financing mode that is suitable to China's actual conditions in the transition period. It is found that a new financing mode is needed to coordinate the development of both direct and indirect financing in China. ? 2011 IEEE

AS160 controls eukaryotic cell cycle and proliferation by regulating the CDK inhibitor p21

<p>AS160 (TBC1D4) has been implicated in multiple biological processes. However, the role and the mechanism of action of AS160 in the regulation of cell proliferation remain unclear. In this study, we demonstrated that AS160 knockdown led to blunted cell proliferation in multiple cell types, including fibroblasts and cancer cells. The results of cell cycle analysis showed that these cells were arrested in the G1 phase. Intriguingly, this inhibition of cell proliferation and the cell cycle arrest caused by AS160 depletion were glucose independent. Moreover, AS160 silencing led to a marked upregulation of the expression of the cyclin-dependent kinase inhibitor p21. Furthermore, whereas AS160 overexpression resulted in p21 downregulation and rescued the arrested cell cycle in AS160-depeleted cells, p21 silencing rescued the inhibited cell cycle and proliferation in the cells. Thus, our results demonstrated that AS160 regulates glucose-independent eukaryotic cell proliferation through p21-dependent control of the cell cycle, and thereby revealed a molecular mechanism of AS160 modulation of cell cycle and proliferation that is of general physiological significance.</p

Stretchable fiber-shaped aqueous aluminum ion batteries

The emerging wearable electronics have significantly motivated the development of fiber-shaped batteries with excellent electrochemical performance, safety, and flexibility. Aluminum (Al) ion batteries are potential candidates due to their high natural abundance, three-electron-redox behavior, and low cost. However, the integration of Al ion battery into wearable electronics remains unexplored. Herein, a stretchable fiber-shaped aqueous Al ion battery is reported, which involves manganese hexacyanoferrate cathode, graphene oxide decorated MoO3 anode, and hydrogel electrolyte. The resulting fiber-shaped battery exhibits good stretching properties and cycling stability (91.6% over 100 cycles at 1 A cm−3). Moreover, by employing a rocking-chair energy storage mechanism, the fiber-shaped battery offers a high specific capacity of 42 mAh cm−3 at 0.5 A cm−3, corresponding to a high specific energy of 30.6 mWh cm−3. As a demonstration, the fiber-based Al ion batteries are integrated into wearable textiles to power LED light, demonstrating the feasibility in stretchable and wearable electronics. (Figure presented.).Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Nanyang Technological UniversityNational Research Foundation (NRF)Published versionThis work was supported by the Singapore Ministry of Education Academic Research Fund Tier 2 (MOE2019-T2-2-127 and MOE-T2EP50120-0002), A*STAR under AME IRG (A2083c0062), the Singapore Ministry of Education Academic Research Fund Tier 1 (MOE2019-T1-001-103 (RG 73/19) and MOE2019-T1-001-111 (RG90/19)) and the Singapore National Research Foundation Competitive Research Program (NRF-CRP18-2017-02). This work was partly supported by the Schaeffler Hub for Advanced Research at NTU, under the ASTAR IAF-ICP Programme ICP1900093. This work was also supported by Nanyang Technological University

Effects of Incorporating Different Proportions of Humic Acid into Phosphate Fertilizers on Phosphorus Migration and Transformation in Soil

Incorporating humic acid (HA) into phosphate fertilizers to produce HA-enhanced phosphate fertilizers (HAPs) can improve the migration and availability of fertilizer-derived phosphorus (P) in soil. However, the optimal proportion of HA remains inconsistent. Here, we investigated the effects of HAPs with different HA proportions (0.1–10% w/w) on water-soluble P fixation rate, P migration, P transformation, and soil microorganisms, and analyzed the main P forms in HAP using Fourier transform infrared spectroscopy and nuclear magnetic resonance spectroscopy. The results showed that incorporating 0.1% HA had no impact on P migration and transformation, whereas incorporating 0.5–10% HA increased the migration distance and cumulative migration of fertilizer-derived P by 0–5 mm and 17.1–30.3%, respectively, compared with conventional phosphate fertilizer (CP). Meanwhile, HAPs with 0.5–10% HA significantly reduced the water-soluble P fixation rate by 18.3–25.6%, and significantly increased the soil average available P (AP) content in 0–40 mm soil layer around the P application site by 6.2–12.9% relative to CP, partly due to the phosphate monoesters in HAPs. Clustering analysis revealed that 0.5% HA had similar effects relative to higher HA proportions (1% and 5%), and the inhibition of HAP with 0.5% HA on bacteria and fungi was also greater than that of CP due to the high concentration of soil P. Overall, 0.5% was determined to be the optimal amount of HA for HAP production, which provided a theoretical basis for the development of high-efficiency phosphate fertilizer

Adenanthin, a Natural ent-Kaurane Diterpenoid Isolated from the Herb Isodon adenantha Inhibits Adipogenesis and the Development of Obesity by Regulation of ROS

Adenanthin, a natural ent-kaurane diterpenoid extracted from the herb Isodon adenantha, has been reported to increase intracellular reactive oxygen species in leukemic and hepatocellular carcinoma cells. However, the function and mechanism of the compound in adipogenesis and the development of obesity is still unknown. In this study, we demonstrated that adenanthin inhibited adipogenesis of 3T3-L1 and mouse embryonic fibroblasts, and the underlying mechanism included two processes: a delayed mitotic clonal expansion via G0/G1 cell cycle arrest by inhibiting the RB-E2F1 signaling pathway and a reduced C/EBP&beta; signaling by inhibiting the expression and activity of C/EBP&beta; during mitotic clonal expansion. Furthermore, adenanthin significantly reduced the growing body weight and adipose tissue mass during high-fat diet-inducing obesity of mice, indicating the beneficial effects of adenanthin as a potential agent for prevention of obesity